With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional... Show moreWith the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models). Show less
Nicotinamide N-methyltransferase (NNMT) catalyzes the methylation of nicotinamide to form N-methylnicotinamide. Overexpression of NNMT is associated with a variety of diseases, including a number... Show moreNicotinamide N-methyltransferase (NNMT) catalyzes the methylation of nicotinamide to form N-methylnicotinamide. Overexpression of NNMT is associated with a variety of diseases, including a number of cancers and metabolic disorders, suggesting a role for NNMT as a potential therapeutic target. By structural modification of a lead NNMT inhibitor previously developed in our group, we prepared a diverse library of inhibitors to probe the different regions of the enzyme’s active site. This investigation revealed that incorporation of a naphthalene moiety, intended to bind the hydrophobic nicotinamide binding pocket via π–π stacking interactions, significantly increases the activity of bisubstrate-like NNMT inhibitors (half-maximal inhibitory concentration 1.41 μM). These findings are further supported by isothermal titration calorimetry binding assays as well as modeling studies. The most active NNMT inhibitor identified in the present study demonstrated a dose-dependent inhibitory effect on the cell proliferation of the HSC-2 human oral cancer cell line. Show less
Zhang, Z.K.; J.J. du; Wang, S.; Shao, L.; Jin, K.; Li, F.; ... ; Zhang, L. 2019
Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the... Show moreNumerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from the desert and anthropogenic emissions. Accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available. Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on past observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably. Show less
In this dissertation, a primitive recursive algorithm is given for the computation of the étale Euler-Poincaré characteristic (which is the alternating sum of the étale cohomology groups in... Show moreIn this dissertation, a primitive recursive algorithm is given for the computation of the étale Euler-Poincaré characteristic (which is the alternating sum of the étale cohomology groups in the Grothendieck group of Galois modules) with finite coefficients, and on arbitrary varieties over a field. For smooth curves, a primitive recursive algorithm is given for the computation of the étale cohomology groups themselves, using a geometric interpretation of the elements of the first etale cohomology. The general case is then reduced to the case of smooth curves by making the standard dévissage techniques explicit. Show less
